Path planning for autonomous parafoils using particle chance constrained rapidly-exploring random trees in a computationally constrained environment

Particle chance constrained rapidly-exploring random trees (PCC-RRT) is a sampling-based path-planning algorithm which uses particles to approximate an uncertainty distribution. In this thesis, we study the use of PCC-RRT on an autonomous parafoil. Specifically, we explore the behavior of PCC-RRT in a computationally constrained environment by studying the tradeoff between the number of samples and number of particles per sample and its effect on miss distance in single-threaded coded with a time constraint. We compare the results generated with the PCC-RRT planner to the equivalent data from a nominal planner using rapidly-exploring random trees (RRT) to determine the effect of robustness.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2012

Author dc:creator

• Klerman, Shoshana

Advisor dc:contributor.advisor

• Jonathan P. How.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/85396

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/85396